Gender Dysphoria, Mental Health, and Poor Sleep Health Among Transgender and Gender Nonbinary Individuals: A Qualitative Study in New York City
Bibliographic record
Abstract
Background: A vast amount of research has demonstrated the numerous adverse health risks of short sleep duration and poor sleep health among the general population, and increasing studies have been conducted among lesbian, gay, and bisexual individuals. However, although poor sleep health is disproportionately experienced by sexual and gender minority populations, little research has examined sleep quality and associated factors among transgender and gender nonbinary (TGNB) individuals. This study qualitatively explored the relationship that factors such as gender identity, mental health, and substance use have with sleep health among a sample of TGNB individuals in New York City. Methods: Forty in-depth interviews were conducted among an ethnically diverse sample who identified as transgender male, transgender female, and gender nonbinary from July to August 2017. All interviews were transcribed, coded, and thematically analyzed for domains affecting overall sleep, including mental health, gender identity, and various coping mechanisms to improve overall sleep. Results: TGNB interview participants frequently described one or more problems with sleeping. Some (15%) participants suggested that mental health issues caused them to have difficulty falling asleep, but that psychiatric medication was effective in reducing mental health issues and allowing them to sleep. An even larger number (35%) told us that their gender identity negatively impacted their sleep. Specifically, participants described that the presence of breasts, breast binding, stress and anxiety about their identity, and concerns about hormonal therapy and gender-affirming surgery were all reported as contributing to sleep problems. Given these sleep challenges, it is not surprising that most (60%) participants used various strategies to cope with and manage their sleep problems, including prescription and over-the-counter sleep medications (33%) and marijuana (18%). Conclusions: Our findings document that sleep health is frequently an issue for TGNB individuals, and they also offer insight into the various ways that TGNB individuals attempt to cope with these sleep problems. Sleep health promotion interventions should be developed for TGNB people, which would promote positive mental health, reduce the risk of pharmaceutical adverse events, and help alleviate psychosocial stress in this target population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".